Vision for Next-Gen Hardware Acceleration

Amitkumar Shrivastava

The author is Global Fujitsu Distinguished Engineer, Fujitsu

AI has evolved from futuristic promise to the driving engine of cross-industry innovation. From enabling self-driving vehicles in complex environments to generating human-like content through advanced models, AI’s impact is undeniable. However, its rapid progression is exceeding the capacity of traditional hardware. Trillion-parameter language models and multimodal systems require computational scale that general-purpose CPUs cannot efficiently support. Designed for versatility, CPUs struggle with parallel workloads and exhibit high power consumption in deep learning tasks.

To address this challenge, specialised accelerators are leading the way. Graphics Processing Units (GPUs), Neural Processing Units (NPUs), Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) are forming the foundation of AI compute. These components are not mere enhancements; they enable real-time AI capabilities across mobile devices, edge systems and hyper-scale datacentres.

The Critical Imperative for Hardware Reinvention

This is not a marginal efficiency issue, it is a foundational constraint that determines AI’s scalability, energy efficiency, and accessibility. Inference at the edge, across devices such as embedded sensors and autonomous platforms demands low-latency, low-power compute. Conversely, training generative AI models in fields like life sciences and manufacturing requires vast parallelization and throughput. These demands expose limitations in memory bandwidth, interconnect latency, and power draw.

Meeting future requirements necessitates more than incremental upgrades. It calls for a comprehensive rethinking of AI’s hardware substrate, reimagined from the chip level upward.

Today’s Chips – The AI Game-Changers

Specialised accelerators are reshaping AI infrastructure. GPUs such as NVIDIA’s H100 and AMD’s MI300X offer massive parallelism and high-bandwidth memory for rapid model training and inference. NPUs, integrated into mobile and edge environments, support real-time inference with optimal energy efficiency. FPGAs provide hardware-level adaptability, while ASICs like Google’s TPU v5e are designed for maximum efficiency in dedicated workloads.

Emerging alternatives are further expanding the ecosystem. Intel’s Gaudi 3 and Cerebras CS-3, leveraging dataflow and wafer-scale architecture respectively, present differentiated approaches to scaling AI compute capacity and throughput.

Despite these advances, challenges persist. Model complexity continues to increase dramatically, often by orders of magnitude in short development cycles. Performance bottlenecks emerge from memory and interconnecting limitations. Power consumption becomes increasingly unsustainable. The next wave of AI acceleration must address these barriers holistically.

Next-Generation Hardware Acceleration

The future of AI performance will be shaped by heterogeneous computing environments, where different accelerators work in coordination and emerging technologies extend the limits of current design paradigms.

At the core of this architecture is dynamic task distribution across CPUs, GPUs, NPUs and ASICs – allocating workloads based on precision, latency and efficiency requirements. This enables scalable, modular solutions for both hyperscale AI in the Cloud and responsive inference at the edge.

Chiplet architectures allow modular assembly of compute units, increasing design flexibility while enhancing scalability and reuse. When combined with High Bandwidth Memory (HBM3e) and NVIDIA’s NVLink Fusion interconnect fabric, they enable high-throughput, low-latency systems tailored to AI workloads. Photonic networking, demonstrated through silicon photonics in solutions such as NVIDIA Spectrum-X and Quantum InfiniBand, advances energy-efficient interconnects that are critical for modern data centers.

Additionally, Analogue In-Memory Computing (AIMC) represents a promising path to minimising data movement by performing operations directly within memory. This has the potential to significantly reduce power consumption and accelerate inference in future workloads. Collectively, these innovations signal a shift in focus from merely accelerating compute to optimizing the full AI systems stack, with energy efficiency as a core metric of success.

Why Software Stack Matters as Much as Silicon

Hardware capabilities must be fully supported by a software stack that enables their practical deployment. Compilers and runtimes such as NVIDIA CUDA, Google XLA and Intel OpenVINO, translate high-level model definitions into optimised hardware instructions. Graph optimisation libraries like ONNX Runtime and TensorRT streamline execution, while orchestration frameworks such as KubeFlow and Ray manage workload distribution across hybrid environments.

This integrated approach to hardware-software co-design is essential to achieve the performance and flexibility demanded by enterprise-scale AI deployments.

Challenges and the Call to Action

Several obstacles remain. Coordinating heterogeneous systems requires advanced runtime orchestration, data flow synchronisation and architectural standardisation. Memory and bandwidth constraints necessitate ongoing innovation. Custom silicon, including ASICs, requires significant financial investment and long development cycles. Furthermore, the environmental impact of large-scale AI workloads is incompatible with long-term sustainability goals.

Geopolitical dynamics are also influencing the semiconductor and AI ecosystem. Nations are prioritising semiconductor sovereignty, secure supply chains and technological self-sufficiency. Programmes such as the US CHIPS Act, the EU Chips Joint Undertaking and India’s Semicon India initiative reflect the increasing strategic importance of domestic innovation. A resilient, collaborative global ecosystem is essential for equitable advancement of AI capabilities.

To progress, the ecosystem must act collectively. Chip manufacturers, AI researchers and system architects must co-design solutions that scale across domains. Governments and alliances must incentivise sustainable hardware research and open hardware standards. Enterprises must invest in adaptable, forward-compatible architectures that support continual evolution.

A Silicon-Powered Future

AI’s full potential cannot be realised without rethinking the infrastructure that supports it. Embracing heterogeneous computing, modular silicon design and integrated software ecosystems will define the next era of AI acceleration. This transition is a strategic inflection point. The question is no longer whether AI will transform industry, but whether we have engineered the systems capable of supporting that transformation. The future depends on our collective ability to innovate, collaborate and reimagine AI from the chip up.